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---
library_name: ultralytics
tags:
- object-detection
- yolo
- yolo9
- animals
- CAN-Benchmark
license: mit
datasets:
- ICICLE-AI/CAN_Benchmark
task: object-detection
---
# YOLOv9 – Animal Detection (Zebra, Impala, Giraffe)
This model is a **YOLOv9** detector fine-tuned with [Ultralytics](https://github.com/ultralytics/ultralytics).
It was trained for **50 epochs** on a **subset** of the [ICICLE-AI/CAN_Benchmark](https://huggingface.co/datasets/ICICLE-AI/CAN_Benchmark) dataset containing three species:
- **0 → zebra**
- **1 → impala**
- **2 → giraffe**
## 📊 Training details
- Framework: Ultralytics YOLOv9
- Epochs: 50
- See full hyperparameters in [`args.yaml`](./args.yaml)
The model converged by ~40 epochs, and shows strong precision/recall on the held-out validation set:
## 🚀 Usage
Load the model directly from the Hugging Face Hub:
```python
from ultralytics import YOLO
# Load model from HF Hub
model = YOLO("ICICLE-AI/yolov9-animals-AE-data")
# Run inference
results = model("demo.jpg")
results[0].show()
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